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richard-churchman
cc0a2bee21
Included open-source Accord.net library code natively to the solution given the deprecated state of that project. Accord.net will be supported inside Jube for own needs (such as upgrading to .Net 7+). Accord.net libraries modified to compile to .Net 6. Modified Accord.net libraries to better support Newtonsoft Json.net serialisation. Included annotations in the Accord.net libraries to house important dependency statistics. Serialised json can be used independently of Jube to recall a Neural Network. Removal of Binary Serializer totally as a precursor to the projects support of .Net 7+. Extensive bug fixing in crucial functions of Neural Network training and recall. While this part of the system remains in Alpha, it is substantially more fit for purpose.
101 lines
3.4 KiB
C#
101 lines
3.4 KiB
C#
// Accord Statistics Library
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// The Accord.NET Framework
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// http://accord-framework.net
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//
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// Copyright © César Souza, 2009-2017
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// cesarsouza at gmail.com
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//
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// This library is free software; you can redistribute it and/or
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// modify it under the terms of the GNU Lesser General Public
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// License as published by the Free Software Foundation; either
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// version 2.1 of the License, or (at your option) any later version.
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//
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// This library is distributed in the hope that it will be useful,
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// but WITHOUT ANY WARRANTY; without even the implied warranty of
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// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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// Lesser General Public License for more details.
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//
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// You should have received a copy of the GNU Lesser General Public
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// License along with this library; if not, write to the Free Software
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// Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
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//
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namespace Accord.Statistics.Analysis.ContrastFunctions
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{
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using System;
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using Accord.Compat;
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/// <summary>
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/// Exponential contrast function.
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/// </summary>
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///
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/// <remarks>
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/// According to Hyvärinen, the Exponential contrast function may be
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/// used when the independent components are highly super-Gaussian or
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/// when robustness is very important.
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/// </remarks>
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///
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/// <seealso cref="IndependentComponentAnalysis"/>
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///
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[Serializable]
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public class Exponential : IContrastFunction
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{
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double alpha = 1;
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/// <summary>
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/// Initializes a new instance of the <see cref="Exponential"/> class.
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/// </summary>
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/// <param name="alpha">The exponential alpha constant. Default is 1.</param>
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///
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public Exponential(double alpha)
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{
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this.alpha = alpha;
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}
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/// <summary>
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/// Gets the exponential alpha constant.
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/// </summary>
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///
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public double Alpha { get { return alpha; } }
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/// <summary>
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/// Initializes a new instance of the <see cref="Exponential"/> class.
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/// </summary>
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///
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public Exponential() { }
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/// <summary>
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/// Contrast function.
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/// </summary>
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///
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/// <param name="x">The vector of observations.</param>
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/// <param name="output">At method's return, this parameter
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/// should contain the evaluation of function over the vector
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/// of observations <paramref name="x"/>.</param>
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/// <param name="derivative">At method's return, this parameter
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/// should contain the evaluation of function derivative over
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/// the vector of observations <paramref name="x"/>.</param>
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///
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public void Evaluate(double[] x, double[] output, double[] derivative)
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{
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// Exponential contrast function and its derivative, as given
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// in original Hyvärinen's paper. See main references for the
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// Independent Component Analysis class for details.
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for (int j = 0; j < x.Length; j++)
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{
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double w = x[j];
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double e = System.Math.Exp(-alpha * (w * w) / 2.0);
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// g(w*x) = wx * exp(-(wx^2)/2)
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output[j] = w * e;
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// g'(w*x) = (1 - wx^2) * exp(-(wx^2)/2)
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derivative[j] = (1.0 - alpha * w * w) * e;
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}
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}
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}
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}
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